pytorch_scrap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%matplotlib qt\n",
    "\n",
    "import torch\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.utils.data import dataloader\n",
    "\n",
    "from preprocessing import Normalize_df, WindSpeedTimeSeriesDataset, ComposeTransform, ToTensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>air_temperature_mean</th>\n",
       "      <th>pressure</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.370203</td>\n",
       "      <td>0.103164</td>\n",
       "      <td>0.732591</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.322799</td>\n",
       "      <td>0.268912</td>\n",
       "      <td>0.838440</td>\n",
       "      <td>0.354167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.302483</td>\n",
       "      <td>0.709078</td>\n",
       "      <td>0.988858</td>\n",
       "      <td>0.260417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.246050</td>\n",
       "      <td>0.850758</td>\n",
       "      <td>0.239554</td>\n",
       "      <td>0.093750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.194131</td>\n",
       "      <td>0.827372</td>\n",
       "      <td>0.345404</td>\n",
       "      <td>0.291667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       time  air_temperature_mean  pressure  wind_direction  wind_speed\n",
       "0  0.000000              0.370203  0.103164        0.732591    0.625000\n",
       "1  0.000011              0.322799  0.268912        0.838440    0.354167\n",
       "2  0.000022              0.302483  0.709078        0.988858    0.260417\n",
       "3  0.000033              0.246050  0.850758        0.239554    0.093750\n",
       "4  0.000044              0.194131  0.827372        0.345404    0.291667"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = Normalize_df(pd.read_csv('./dataset-daily.csv'))\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_v = 0\n",
    "to_v = 200\n",
    "\n",
    "#plt.plot(dataset['air_temperature_mean'].values[from_v:to_v], label = 'air_temperature_mean')\n",
    "#plt.plot(dataset['pressure'].values[from_v:to_v], label = 'pressure')\n",
    "plt.plot(dataset['wind_direction'].values[from_v:to_v], label = 'wind_direction')\n",
    "plt.plot(dataset['wind_speed'].values[from_v:to_v], label = 'wind_speed')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[0.3702, 0.1032, 0.7326],\n",
       "         [0.3228, 0.2689, 0.8384],\n",
       "         [0.3025, 0.7091, 0.9889],\n",
       "         [0.2460, 0.8508, 0.2396],\n",
       "         [0.1941, 0.8274, 0.3454],\n",
       "         [0.1670, 0.6671, 0.3398]], dtype=torch.float64),\n",
       " tensor([[0.6250, 0.3542, 0.2604, 0.0938, 0.2917, 0.2396]], dtype=torch.float64))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = WindSpeedTimeSeriesDataset(dataset, window_size=6, transform=ComposeTransform([ToTensor()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "\n",
    "trainloader = dataloader.DataLoader(train_dataset, batch_size, shuffle = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features Size: torch.Size([32, 6, 3])\n",
      "Labels Size: torch.Size([32, 1, 6])\n"
     ]
    }
   ],
   "source": [
    "f, l = next(iter(trainloader))\n",
    "f = f.type(torch.FloatTensor)\n",
    "\n",
    "print(f'Features Size: {f.shape}')\n",
    "print(f'Labels Size: {l.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = 3\n",
    "\n",
    "conv1 = nn.Conv1d(6, 18, 1)\n",
    "pool1 = nn.MaxPool1d(1)\n",
    "        \n",
    "fc2 = nn.Linear(18 * input_size, 18)\n",
    "        \n",
    "lstm3 = nn.LSTM(input_size= 18,\n",
    "                            hidden_size=36,\n",
    "                            num_layers=1,\n",
    "                            batch_first= True,\n",
    "                            dropout = 0)\n",
    "fc4 = nn.Linear(36, 18)\n",
    "fc5 = nn.Linear(18, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden = (torch.zeros(1, 32, 36),\n",
    "                torch.zeros(1, 32, 36))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 1, 18])\n",
      "torch.Size([32, 1, 36])\n",
      "torch.Size([32, 1, 6])\n"
     ]
    }
   ],
   "source": [
    "x = pool1(conv1(f))\n",
    "        \n",
    "x = x.view(x.size(0), -1)\n",
    "x = fc2(x)\n",
    "x = x.unsqueeze(1)\n",
    "print(x.shape)\n",
    "x, hidden = lstm3(x, hidden)\n",
    "print(x.shape)\n",
    "x = fc4(x)\n",
    "x = fc5(x)\n",
    "print(x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 18, 3])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = conv1(f)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 18, 30])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, hidden = lstm1(x, hidden)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 54])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = x.view(x.size(0), -1)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 6])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = fc(x)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 1, 6])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = x.unsqueeze(1)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, hidden = lstm1(x, hidden)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 1, 30])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = fc2(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 1, 50])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc3 = nn.Linear(50, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = fc3(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 1, 6])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = nn.MSELoss()(x, l.type(torch.FloatTensor))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.0765, grad_fn=<MseLossBackward>)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.0765, grad_fn=<MseLossBackward>)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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